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Category: robotics/AI – Page 11

Advancing neuroscience research with high-speed, automated electrophysiology

Understanding the electrical activity of neurons is key to unlocking insights into neurological diseases. Yale researchers have unveiled a high-throughput automated method that captures the electrical activity of large numbers of neurons simultaneously and without bias.

This cutting-edge approach provides a powerful “functional fingerprint” of neuron populations in their natural state, opening new doors to understanding and treating neurological diseases. The work was published June 13 in Nature Protocols.

The patch-clamp technique has long been a gold standard for studying the electrical activity of neurons, the fundamental units of the nervous system. However, the manual execution of this approach is slow and labor-intensive. Recent advances in robotic patch-clamp technologies have improved speed and efficiency, but they are limited to artificially grown neurons rather than neurons in their native unmanipulated state.

AI Uncovers Wild Spin of the Milky Way’s Supermassive Black Hole

Back in 2019, the Event Horizon Telescope (EHT) team revealed the first-ever image of a supermassive black hole in the galaxy M87. In 2022, they followed up with the iconic image of Sagittarius A at the heart of the Milky Way. While these images were groundbreaking, the data behind them held even deeper insights that were hard to decode.

Neural Networks Meet Black Hole Physics

Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files. Funded by the National Science Foundation (NSF) as part of the Partnership to Advance Throughput Computing (PATh) project, the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network, which can quantify uncertainties. This allowed the researchers to make a much better comparison between the EHT data and the models.

“If Anthropic Succeeds, Everything Changes”: A Nation of Benevolent AI Geniuses Could Rise and Reshape the Future of Human Civilization

IN A NUTSHELL 🌟 Anthropic is on a mission to develop artificial general intelligence that remains ethical and benevolent. 🎯 The company’s strategy, known as the Race to the Top, aims to set global standards for safe AI development. 🤖 Claude, Anthropic’s AI model, is designed to embody ethical principles and serve as a constant.

Self-powered artificial synapse mimics human color vision

As artificial intelligence and smart devices continue to evolve, machine vision is taking an increasingly pivotal role as a key enabler of modern technologies. Unfortunately, despite much progress, machine vision systems still face a major problem: Processing the enormous amounts of visual data generated every second requires substantial power, storage, and computational resources. This limitation makes it difficult to deploy visual recognition capabilities in edge devices, such as smartphones, drones, or autonomous vehicles.

Interestingly, the human visual system offers a compelling alternative model. Unlike conventional machine vision systems that have to capture and process every detail, our eyes and brain selectively filter information, allowing for higher efficiency in visual processing while consuming minimal power.

Neuromorphic computing, which mimics the structure and function of biological neural systems, has thus emerged as a promising approach to overcome existing hurdles in computer vision. However, two major challenges have persisted. The first is achieving color recognition comparable to human vision, whereas the second is eliminating the need for external power sources to minimize energy consumption.